Integrated serum and urinary metabolomics reveal distinct signatures of kidney graft function—a single-center cohort study

Abstract

Kidney transplantation remains the gold standard treatment of end-stage kidney disease; however, its recipients exhibit mixed clinical trajectories, with variable renal function and proteinuria evolution over time. Currently used methods to screen these changes are associated with possible adverse effects and limitations. Methods: In this single-center cohort study, 174 adult kidney transplant recipients were enrolled between January 2024 and January 2025 and followed for 12 months. Patients underwent serum and urinary metabolomic profiling using nuclear magnetic resonance (NMR) spectroscopy. Furthermore, clinical and biological data were collected, allowing for assessment of changes in renal function and proteinuria, with patient stratification based on outcome trends. Results: Serum metabolomic profiles were linked to renal outcomes. For ΔeGFR, valine differed between patients with positive and negative trajectories, while GlycA, GlycB and Glyc/SPC were associated with ΔeGFR in adjusted models. For Δproteinuria, GlycA, VLDL particles and triglycerides showed correlations, while glucose differed between patients with improving and worsening proteinuria. In adjusted models, isoleucine, alanine, GlycA and Glyc/SPC were associated with Δproteinuria. Urinary metabolomic profiles showed fewer associations. Dimethylamine and urinary creatinine were associated with ΔeGFR in adjusted models, while acetic acid differed between proteinuria groups. No urinary metabolite was associated with Δproteinuria after adjustment. Conclusions: Overall, metabolomic changes were connected with renal outcomes in kidney transplant patients, with distinct but partially overlapping patterns for ΔGFR and Δproteinuria. Serum findings were more consistent, while urinary associations were more limited. Further longitudinal and externally validated studies are required to confirm these observations and explore the potential of this metabolomic approach in the search for novel biomarkers.